In 360 stereo domain, SweepNet [1]} and
OmniMVS [2]} estimated depth from multiple wide-baseline fisheye cameras.
Another recent work is 360SD-Net [3]} which predicted disparity/depth from a pair of ERP images that are taken by a top-bottom camera pair. They [3]} incorporated polar angles to solve distortions and proposed learnable shifting filters to adjust the step size in disparity cost volume construction. However, the learnable shifting filters create extra overhead during training.
In this paper, we propose a closed-form solution, Spherical Warping Layer (SWL), that does not require additional training overhead. Our experiments (Table REF ) show that SWL can significantly improve the performance of 360 stereo matching.
